Joint channel and noise variance estimation in a wideband OFDM system

ABSTRACT

A method and system for use in a wireless-local-area network (WLAN), for simultaneously estimating the unknown multi-path channel and noise characteristics and using the channel and noise estimates to improve system performance in the presence of narrowband interferers. Estimates are made for the unknown multi-path channel and noise characteristic without a-priori knowledge of the location of the interference in the band and this information is used to generate soft-metrics for a Viterbi decoder. By using the improved channel and noise estimates, the packet error rate (PER) of an 802.11g WLAN system may be maintained despite collisions with interfering packets thereby allowing the 802.11g system to be less sensitive to the interference.

The present invention relates generally to communication systems, andmore particularly to an improved system and associated method forperforming narrowband interference cancellation in a wideband orthogonalfrequency modulation local area network.

The IEEE 802.11 WLAN standard provides a number of physical (PHY) layeroptions in terms of data rates, modulation types and spreading spectrumtechnologies. Three physical layers were standardized in the initialrevision of 802.11. They include a direct sequence (DS) spread spectrumPHY, a frequency-hopping (FH) spread spectrum PHY and an infrared light(IR) PHY. All three architectures are designed for operation in the 2.4GHz band.

A second extension to the 802.11 standard, namely IEEE 802.11b, definesrequirements for a physical layer based on direct sequence spreadspectrum/complementary code keying (DSSS/CCK) for operation in the 2.4GHz ISM frequency band, for data rates up to 11 Mbps. When the original802.11b specification was approved, the IEEE concurrently approved thespecs for 802.11a which was designed to use a PHY layer based on theorthogonal frequency division multiplexing (OFDM) for operation in the 5GHz U-NII frequency for data rates ranging from 6 Mps to 54 Mps.

In November of 2001, the IEEE 802.11 committee adopted a draft standard,i.e., 802.11 g/D2.1, that proposes to reuse the OFDM physical layer(PHY) which is currently being used as the 802.11a standard in the 5 GHzband, for use in the 2.4 GHz band. A complete description of the 802.11gstandard can be found in IEEE 802.11 g/D2.1, “Draft supplement to802.11-1999, Wireless LAN MAC and PHY specifications: FurtherHigher-Speed Physical Layer (PHY) extensions in the 2.4 GHz band,”incorporated by reference in its entirety. As is well known, the 802.11gstandard uses bit interleaved coded modulation (BICM) in conjunctionwith orthogonal frequency division modulation (OFDM) to combat theeffects of multi-path fading.

One drawback of adopting the OFDM PHY layer for use in the 2.4 GHz bandis that the operating environments in the 2.4 GHz and 5 GHz bands arevery different and hence implementations developed for 5 GHz, if useddirectly at 2.4 GHz may cause system degradation. In particular, onesignificant operating environment difference of note is the presence ofBluetooth systems in the 2.4 GHz band. Bluetooth is a computing andtelecommunications industry specification that describes how mobilephones, computers, and personal digital assistants (PDAs) can easilyinterconnect with each other and with each other and with home andbusiness phones and computers using a short-range wireless connection. Adetailed description of Bluetooth can be found in K. V. S. S. S. SSairam, et al., “Bluetooth in wireless communications,” IEEECommunications Magazine, vol. 40, no. 6, pp. 90-96, June 2002,incorporated herein by reference in its entirety. Bluetooth systems arenarrow band (i.e., 1 MHz bandwidth), frequency-hopped systems. Bycontrast, WLANS are wideband (i.e., 22 MHz bandwidth) systems with nofrequency hopping. Studies have shown that the effect of Bluetoothinterference on WLANs can be catastrophic in the case of collisions,i.e., in the case where a Bluetooth packet collides with an 802.11packet, the error rate of the latter is very high. One such study can befound in I. Howitt, “WLAN and WPAN coexistence in UL band,” IEEEtransactions Veh. Tech., vol. 50, no. 4, pp. 1114-1124, July 2001,incorporated by reference, which shows that the performance of WLANSoperating in accordance with 802.11g degrades dramatically in thepresence of narrowband interferers such as Bluetooth. While interferenceavoidance mechanisms in the MAC layer can be useful, they are anincomplete solution in that they limit the available throughput of theWLAN system.

Therefore, there is a need for a PHY layer algorithm that allows a802.11g WLAN system to be more robust in the presence of interferencesuch as bluetooth interference.

The present invention is directed to a method and system for use in awireless-local-area network (WLAN), for simultaneously estimating theunknown multi-path channel and noise characteristics and using thechannel and noise estimates to improve system performance in thepresence of narrowband interferers. The present invention estimates theunknown multi-path channel and noise characteristic without a-prioriknowledge of the location of the interference in the band and uses thisinformation to generate soft-metrics for a Viterbi decoder. By using theimproved channel and noise estimates, the packet error rate (PER) of an802.11g WLAN system may be maintained despite collisions withinterfering packets thereby allowing the 802.11g system to be lesssensitive to the interference.

Currently, conventional schemes for providing interference cancellationtry to avoid collisions between interfering systems, such as Bluetooth,by using cooperative methods employed at the MAC layer. Avoidingcollisions, however, has the disadvantage of lowering the overallbit-rate of the WLAN system, only allowing transmissions betweenbluetooth transmissions. There has been very little research oninvestigating methods of interference cancellation at the PHY layer. Thepresent invention addresses this need by providing a method ofinterference cancellation defined at the PHY layer that allows thepacket error rate (PER) of an 802.11g system to be maintained in thepresence of bluetooth interference.

In a preferred embodiment, the present invention provides an improvedmethod for estimating the multi-path channel and interferencecharacteristics for use in a convolutional decoder at the PHY layer toimprove system performance in the presence of narrowband interferencefrom systems such as Bluetooth.

A more complete understanding of the method and apparatus of the presentinvention may be had by reference to the following detailed descriptionwhen taken in conjunction with the accompanying drawings wherein:

FIG. 1 illustrates a representative network whereto embodiments of thepresent invention may be applied;

FIG. 2 a illustrates the format of an IEEE 802.11g data packet 30according to the IEEE 802.11g standard;

FIG. 2 b is a more detailed illustration of the construction of the PLCPpreamble field of the data packet of FIG. 2 a;

FIG. 2 c is a detailed illustration of the construction of the two longtraining sequences, i.e., (L₁, L₂) of FIG. 2 b;

FIG. 3 illustrates the construction of a typical network node;

FIG. 4 illustrates a transmitter portion 50 of the PHY unit 46 forperforming the Tx functions in accordance with the prior art;

FIG. 5 is a block diagram illustrating those elements which make up thereceiver portion of the PHY unit of FIG. 4 for performing the Rxfunctions;

FIG. 6 illustrates the matrix components which make up the channelimpulse time/frequency relation;

FIG. 7 illustrates the noise correlation matrix, R_(n);

FIG. 8 is a flowchart describing the steps for obtaining a more refinednoise estimate in accordance with an embodiment of the invention;

FIG. 9 is a diagram of the receiver of FIG. 5 modified to incorporate anadvanced slicer in accordance with an embodiment of the invention; and

FIG. 10 is a flowchart describing the steps for obtaining a more refinednoise estimate in accordance with a second embodiment of the invention.

In the following description, for purposes of explanation rather thanlimitation, specific details are set forth such as the particulararchitecture, interfaces, techniques, etc., in order to provide athorough understanding of the present invention. For purposes ofsimplicity and clarity, detailed descriptions of well-known devices,circuits, and methods are omitted so as not to obscure the descriptionof the present invention with unnecessary detail.

FIG. 1 illustrates a representative network whereto embodiments of thepresent invention may be applied. As shown, a BSS network 10 includes aplurality of network nodes (e.g., AP, STA₁, STA₂, STA₃, and STA₄). Itshould be noted that the network shown in FIG. 1 is small for thepurpose of illustration. In practice, most networks would include a muchlarger number of mobile STAs. It is also noted that while FIG. 2 and thefollowing description are provided with reference to a BSS network, theprinciples of the invention apply equally to an IBSS network. In thenetwork of FIG. 1, during a communication between at least two of thenetwork nodes over air, a first network node (e.g., AP) serves as atransmitting network node and at least one second network node (e.g.,STA₂) serves as a receiving network node for the purpose of transmittingdata packets therebetween.

FIG. 2 a illustrates the format of an IEEE 802.11g data packet 30according to the IEEE 802.11g standard. A data packet can be of variablelength, and is typically around 500-1500 bytes, corresponding to severalOFDM frames. The data packet 30 shown has a format including three mainfields: (1) a physical layer convergence procedure (PLCP) preamble field32, (2) the signal field 34 and (3) the data field 36.

FIG. 2 b is a more detailed illustration of the construction of the PLCPpreamble field 32 of the data packet 30 of FIG. 2 a. The preamble field32 has a duration of 16 μsec and is comprised of ten repetitions of ashort training sequence (i.e., S₁-S₁₀) and two repetitions of a longtraining sequence (L₁, L₂). The ten repetitions of the short trainingsequence S₁-S₁₀ serve to provide synchronization and timing at thereceiver, the details of which are not applicable to the presentinvention. The two long training sequences (L₁, L₂) will be describedbelow with reference to FIG. 2 c. The signal 34 field of data packet 30is comprised of one OFDM frame consisting of 24 bits which convey thedata rate and the length of the data packet 30. The data field 36 ofpacket 30 is comprised of a variable number of OFDM frames using themode specified in the signal field 34. The data field 36 contains thedata bits that are to be transmitted from a transmitting node (e.g., AP)to a receiving node (e.g., STA₁) in the network 10.

FIG. 2 c is a detailed illustration of the construction of the two longtraining sequences, i.e., (L₁, L₂) of FIG. 2 b. The two long trainingsequences (L₁, L₂) are essential to performing the method of theinvention, as will be described below. As shown in FIG. 2 c, eachtraining sequence (L₁, L₂) is comprised of 48 “known” data bits, a₁through a₄₈. That is, both the transmitter and receiver have a-prioriknowledge of the values of the data bits a₁ through a₂₄ and use theknowledge to derive a channel estimate.

Typically, only the first long training sequence, L₁, is used to derivea channel estimate, thereafter the channel estimate may be furtherrefined by utilizing the second long training sequence, L₂ and averagingthe results.

Referring now to FIG. 3, the construction of a typical network node 40is shown to include a processor 42, a media access control (MAC) unit 44connected to the processor 42 by a data interface 43, a physical layer(PHY) unit 46 connected to the MAC unit 44 by a MAC-to-PHY I/O bus 45.As discussed above, the present invention is preferably implemented asan algorithm in the PHY unit 46 of network node 40 in contrast to priorart approaches which have been implemented at the MAC layer 44.

FIG. 4 illustrates a transmitter portion 50 of the PHY unit 46 forperforming the Tx functions in accordance with the 802.11g standard. Theoperations to be described with reference to FIG. 4 are well known anddescribed in detail in the IEEE 802.11g standard. As shown, thetransmitter 50 portion includes a scrambler 51, a convolutional encoder52, an interleaver 53, a bit-to-symbol encoder 54, a serial-to-parallelconverter 55, an IFFT unit 56, a parallel-to-serial converter 57 and aguard interval generator unit 59.

During a data transmit process, The MAC interface 24 provides the databits b_(i) via the MAC-to-PHY I/O bus 26 to the scrambler 51. Thescrambler 51 ensures that the data as presented to the input of theconvolutional encoder 52 is substantially random in pattern. Theconvolutional encoder 52 encodes the scrambled data pattern in a forwarderror correction code and the bit interleaver 53 subsequentlyinterleaves the encoded data. As is well known in the art, theconvolutional encoder 52 is provided with a puncturing block forconverting the convolutional encoder's output from a ½ coding rate tosome other coding rate, e.g., ⅔, from the basic code. The interleavedencoded bits, output from the Interleaver 53, are input to abit-to-symbol encoder 54 which groups the interleaved/encoded bits intodata symbols, a_(k), of a predetermined length as specified by themodulation mode or type. The data symbols, a_(k), are then supplied to aserial-to-parallel converter 55 in a group of N symbols where N=48 datasymbols plus 12 zero-fill symbols in the present 802.11g embodiment. Thesymbol stream that is output from the serial-to-parallel converter 57 issupplied as input to an IFFT unit 56 and are processed therein totransform the N supplied data symbols from the frequency domain to thetime domain.

In the present embodiment, at each iteration, the IFFT unit 56 outputsN=64 complex values in parallel. The 64 complex numbers output from theIFFT unit 56 are supplied as input to a parallel-to-serial converterunit 57 which outputs a serialized stream S₁.

The serialized stream S₁ is then supplied as input to a guard intervalunit 58. Due to the long symbol duration in an 802.11g system,inter-symbol interference may be caused by the channel time dispersionwhich can be eliminated by using a guard interval as a prefix to everytransmitted data packet. In order to maintain the orthogonality of thedata packets, the content of each prefix is a copy of the last part ofthe current data packet, thus making each data packet seem partiallycyclic. As such, the guard interval is conventionally referred to as acyclic prefix. The length of the cyclic prefix is chosen to be greaterthan the length of the channel impulse response. In the presentembodiment, for an 802.11g system, the cyclic prefix is chosen to be 16FFT symbols (0.8 μsec) which gives a total length of 4 μsec for eachOFDM frame duration. It is noted, however, that the cyclic prefix lengthmay be greater than or less than 16 symbols in alternate embodiments.

The modified symbol stream S₁′ now consists of 80 complex symbols (16appended cyclic prefix symbols plus 64 data symbols (48 data symbols+12zero-fill symbols) supplied from the IFFT unit 56), which is thenmodulated for transmission by the modulator 59 over the wireless mediumin accordance with one of the defined OFDM modulation formats or types.

FIG. 5 is a block diagram illustrating those elements which make up thereceiver 60 portion of the PHY unit 22 for performing the receiver (Rx)functions. As shown, the receiver 60 includes a guard stripping unit 61for stripping out the guard interval, i.e., the 16 cyclic prefix symbolswhich were appended at the transmitter 50. What remains thereafter isthe original symbol stream comprised of 64 complex data symbols. Next,the stripped down data stream of 64 complex data symbols are supplied toa serial-to-parallel converter 63 which outputs the 64 complex symbolsto the Fast Fourier transform (FFT) unit 65 which transforms the 64complex symbols from the time to the frequency domain, one value foreach frequency bin, k.

It is noted that in the in the present 802.11g embodiment, the FFT sizeis 64, which represents the number of carriers, k. Of course, one ofordinary skill in the art would recognize that the size of the FFT maybe different for different applications. The 64 complex values in thefrequency domain are output from the FFT unit 65 and provided as inputto a parallel-to-serial unit 66 for conversion back to a serializedstream. The serialized stream output from the parallel-to-serial unit 66is simultaneously provided to the bit metric unit 67 and to the slicerand noise variance estimator unit 68. The noise variance estimator 68performs two operations on the serialized stream. A first operation isto slice each data symbol a_(k) in the stream to its nearestconstellation point. A second operation is to compute a noise varianceestimate. The sliced data symbols and noise variance estimate areprovided as inputs to the bit metric unit 67 which computes soft-metricvalues for each of the 1, 2, 4, or 6 bits (b₀ through b₅) which make upa sliced data symbol a_(k). A sliced data symbol may include 1, 2, 4 or6 bits depending upon the particular application. As is well known tothose in the art, the transmitted symbol a_(k) can be derived from anyof the well-known constellations including, BPSK, QPSK, 16QAM or 64 QAMin which a_(k) represents 1, 2, 4 or 6 bits respectively.

Soft-metric values are computed in the Bit Metric Unit 67 andde-interleaved in the de-interleaver 69. The de-interleaved values arethen provided to the Viterbi decoder 71. It is noted that soft-metricvalue are computed by the bit metric unit 67 as a requirement of theViterbi decoder 71.

The inventors recognize that at point “A” in the receiver the receivedsignal r_(k) at frequency bin k has the general form:r _(k) =H _(k) a _(k) +n _(k) ,k=1, . . . , N  (1)Where:

r_(k) is a received signal at frequency bin k;

H_(k) represents the channel value at frequency bin k;

a_(k) represents the actual value of the transmitted symbol which isknown by the receiver (i.e., a_(k) is a symbol from L₁, the longtraining sequence);

n_(k) represents the noise at frequency bin k with variance σ_(k) ²; and

N represents the number of carriers (i.e., the FFT size).

Equation (1) is a generalized expression for a received signal, r_(k)which results from the transmission of a known symbol a_(k) multipliedby a channel factor H_(k) plus any additive noise, n_(k). Data symbolsa_(k) in equation (1) are transmitted as part of the long trainingsequence portion of a data packet (see FIG. 2 c) are known a-priori atboth the transmitter and receiver for the purpose of estimating thechannel characteristic H_(k).

To calculate the soft-metric, first define the subset of constellationpoints C^(p) _(i) as the set of symbols from the defined constellationsuch that b_(i)=p where p is either 0 or 1. A first step is to find twosymbols a_(0,i) and a_(1,i) for each bit b_(i) as shown in equations (2)and (3):a _(0,i)=arg min|a _(k) εC _(i) ^(a) ∥r _(k) −H _(k) a _(k)|²/σ_(k)²  (2)a _(1,i)=arg min|a _(k) εC _(i) ^(a) ∥r _(k) −H _(k) a _(k)|²/σ_(k)²  (3)Where: a_(0,i) is the probability that the ith bit is a zero; and

-   -   a_(1,i) the probability that the ith bit is a one.

The soft-metric, m_(k)(b_(i)) can then be calculated as:

$\begin{matrix}{{m_{k}(b)} = \frac{{{r_{k} - {H_{k}a_{0,1}}}}^{2} - {{r_{k} - {H_{k}a_{1,i}}}}^{2}}{a_{k}^{2}}} & (4)\end{matrix}$

An important observation regarding equation (4) is that, in aconventional receiver, such as the one shown in FIG. 5, the noise isassumed to be white. Specifically, the noise variance term, σ_(k) ²,shown in the denominator of equation (4) is assumed to be a constant forall frequencies, k, and is ignored. However, in the case whereinterference is present in the band, such as Bluetooth interference, thenoise variance is not a constant but instead varies with frequency.Accordingly, some number of frequency bins, k, have a higher noise valuethan others. Therefore, in the case of interference being present in theband, the noise variance term, σ_(k) ², cannot be neglected. Doing sowould result in severely degraded performance.

The inventors have recognized the need to account for the presence ofinterference in the band and have created a simplified interferencemodel. In the simplified interference model, it is assumed that aBluetooth system is operating at 1 MHz in the same band as an 802.11gsystem. In this scenario, each transmitted 802.11g packet would have 3consecutive frequency channels, k_(i) to k_(i+2), from among the N=64channels of operation that would include additional Gaussian noiseinterference with a variance of σ_(b) ². In accordance with thesimplified interference model, a channel estimate may be developed, aswill be described hereafter.

In accordance with the prior art approach for deriving a channelestimate, equation (1) is solved for H_(k) while ignoring the noiseterm, n_(k), which is assumed to be white gaussian noise (AWGN) withzero mean and variance. Solving equation (1) for H_(k) under anassumption of white noise yields:H _(k) =r _(k) /a _(k)  (6)

The noise term, n_(k), may be ignored in those cases where the noise isassumed to be flat across the band, i.e., AWGN. Under this assumption,the channel value or response H_(k) at each frequency bin, k, isindependent of the response at every other frequency bin.

It is to be appreciated, however, that while the assumption of noisebeing flat across the band simplifies the channel estimate, it suffersin two important respects. First, by using a cyclic prefix length of 16symbols, it is assumed that the impulse response of the channel is notvery wide in time. Because of the linearity between the frequency andtime domain, 16 independent samples in time correspond to 16 independentsamples in frequency. Therefore, even though the FFT size is 64 in802.11g, only 16 of the 64 samples in frequency are independent samples.The conventional “simplified” channel estimate of equation (6) does nottake this correlation into consideration.

A second drawback of using the simplified channel estimate of equation(6) is that all information about the noise term is disregarded. This iscommonly referred to in the art as zero forcing or equalizing.

The present invention overcomes the stated drawbacks by providing anestimate for the noise term. Providing a noise estimate is particularlyadvantageous in the situation where there is interference present in theband, such as bluetooth interference, the problem to which the presentinvention is particularly directed.a _(0,i)=arg min|a _(k) εC _(i) ^(a) ∥r _(k) −H _(k) a _(k)|²/σ_(k)²  (2)a _(1,i)=arg min|a _(k) εC _(i) ^(a) ∥r _(k) −H _(k) a _(k)|²/σ_(k)²  (3)

FIRST EMBODIMENT FOR DERIVING A CHANNEL ESTIMATE

An embodiment of the invention is now described for simultaneouslyestimating the channel and noise in the presence of narrowbandinterference (e.g., Bluetooth interference) and thereby improving thesystem performance.

The inventors recognize that the channel impulse response in the timedomain has a corresponding structure in the frequency domain which is aFourier structure. In the frequency domain, the Fourier transform of thechannel impulse response, h_(i) may be written as:

$\begin{matrix}{{H_{k} = {{\sum\limits_{n = 0}^{{Ne} - 1}\;{h_{n}{\exp\left( {{f2}\;\pi\;{{nk}/N}} \right)}k}} = {0_{n}\mspace{11mu}\ldots}}}\mspace{11mu},{N - 1}} & (7)\end{matrix}$Equation (7) can be re-written in matrix form as a time/frequencyrelation as:[H]=[F][h]  (8)

FIG. 6 illustrates an expanded view of the elements of the matrices ofequation (8). As shown, the channel impulse response in frequency [H],is shown to be an (N×1) (e.g., 64×1) matrix, matrix [F] is an N×N_(c)(e.g., 64×16) truncated Fourier matrix and is multiplied by matrix [h]which is an (N×1) (e.g., 64×1) matrix representing the channel responsein the time domain. It is noted that, for the present embodiment, matrix[h] includes only 16 non-zero values, h₀-h₁₅, which correspond to thenumber of independent variables in time. The 16 values correspond to thelength of the cyclic prefix.

Substituting the time/frequency matrix relation of equation (8) intoequation (1) and re-writing a_(k) in matrix form yields a matrixsolution for the received signal model at point “A” in the receivingchain (see FIG. 5, pt. “A”):r=[A][F][h]+[n]  (9)Where: A is an N×N diagonal matrix composed of the known transmittedsymbols a_(k).

Both matrices [A] and [F] are known a-priori for the training frame.Defining R_(n) to be the correlation matrix of the noise vector [n], and[G]=[A][F], the least-squares estimate of the channel impulse responsevector and frequency response vector may be written as follows:{circumflex over (h)} _(LS)=(G ^(H) R _(n) ⁻¹ G)⁻¹ G ^(H) R _(n) ⁻¹ r  (10){circumflex over (H)} _(LS) =F(G ^(H) R _(n) ⁻¹ G)⁻¹ G ^(H) R _(n) ⁻¹ r  (11)

Two observations may be made from equations (10) and (11). First, giventhat the cyclic prefix length Nc=N, and the noise correlation matrix,R_(n)=σ²I, where I is the identity matrix, equation (11) may be reducedto equation (6), the “simplified” channel estimate. Second, with theexception of the noise correlation matrix, R_(n), all of the matricesrequired in the frequency estimate of the channel, i.e., H_(k), areknown beforehand and can be pre-computed at the receiver. That is, bothmatrices [A] and [F] and therefore [G] are known a-priori for thetraining frame, L₁. Also, r is known as the received vector. The onlyunknown in equation (11) is the noise correlation matrix, R_(n).Therefore, if white noise is assumed, the receiver simply needs toperform one matrix-vector multiplication with the received vector r, toobtain the channel estimate as follows:Ĥ _(LS) =F(G ¹¹ G)⁻¹ G ¹¹ r   (12)

The present invention takes advantage of these two stated observationsso as to derive a channel and noise estimate in accordance with themethod of the invention. Specifically, the method may be generallycharacterized as a two-step approach. First, a simplified channelestimate is made assuming white noise (despite the actual presence ofinterference in the band). Second, having derived a channel estimateunder the assumption of white noise at the first step, the noise maythen be easily estimated. Each step is described in detail below.

In accordance with the first embodiment for making a channel and noiseestimate in an interference environment, a simplified channel estimateis first derived assuming white noise. An assumption of white noise inan actual interference environment is a reasonable one to obtain achannel estimate by considering the noise correlation matrix, R_(n), ofFIG. 7. In the case of narrowband interference, only a small percentageof the total number of values in the noise correlation matrix, R_(n)will have higher noise values. For example, in the specific case ofnarrowband Bluetooth interference, it may be shown that only 3 of the 64frequency noise variance terms in the correlation matrix R_(n) will havehigher noise variance values. Given this relatively low percentage,i.e., 0.047, an initial assumption of white noise so as to obtain achannel estimate is both reasonable and justifiable for the reasonsstated.

Under the assumption of white noise, the noise correlation matrix, R_(n)of equation (11), as illustrated in FIG. 7, becomes an identity matrix Iand the receiver simply needs to perform one matrix-vectormultiplication with the received vector, r, to obtain the simplifiedchannel estimate H_(k). Equation (11) reduces to equation (12) under thewhite noise assumption.Ĥ _(LS) =F(G ¹¹ G)⁻¹ G ¹¹ r   (12)

Having made a channel estimate at a first step of the method, the noisevariance estimate must then be determined. To do so, the channelestimate as computed by equation (12) at the first step, is nowsubstituted back into equation (1). The noise variance at each frequencycan be estimated as follows. Using the previously determined channelestimate, define e to be the error vector:e=r−AĤ _(LS)Then, the noise variance estimate is derived from the error vector as:σ_(k) ² =|e _(k)|²  (14)

The channel and noise variance estimates in frequency, as denoted inequations (11) and (14) may then be averaged over the two long trainingframes, L1 and L2 included each data packet for each frequency bin k.

FIRST EMBODIMENT OF AN IMPROVED NOISE ESTIMATE

It has been experimentally determined that the channel estimate, ascomputed in equation (12), provides a satisfactory estimate whenaveraged over the two long training sequences. However, it has also beendetermined that the noise variance estimate, as computed in equation(14), does not provide a satisfactory estimate when averaged over thetwo long training sequences (L1 and L2) due to the fact that the noiseis a more random process. As such, the noise needs to be furtheraveraged in order to reduce the variance of the estimate.

As described above, the channel and noise variance estimates, i.e.,equations (12) and (14), were obtained from the two long trainingintervals (L₁ and L₂) contained in the PLCP preamble portion 32 (SeeFIG. 3) of the data packet 30. Once the channel and noise estimates areobtained using training intervals (L₁ and L₂), only the data frameportion 36 of packet 30 is available to obtain a more refined noiseestimate. In this regard, having only the data frame portion 36 ofpacket 30 available for making a more refined noise estimate isproblematic in that the data frame portion 36, unlike the PLCP preambleportion 32, does not include any known data symbols (e.g., a₁ througha₂₄). Therefore, obtaining a more refined noise estimate requires afurther processing step. Namely, the transmitted symbols of the dataframe portion 36 must first be estimated (because they are not known bythe receiver) as a pre-requisite to obtain the more refined noiseestimate. FIG. 8 is a flowchart describing the steps for obtaining animproved noise estimate.

At step 900, estimate Ĥ from equation (12) and σ² from equation (14) onthe two training frames (L₁ and L₂).

At step 920, during the ith OFDM data frame contained in the data frameportion 36 of packet 30, use the channel estimate H(cap)LS obtained atstep 900 to estimate the transmitted data symbol at frequency k and timeI as follows:â _(i,i) =r _(k,i) /Ĥ _(k)â_(i,i)=ā_(k,i) sliced to nearest constellation pointAs stated above, data symbol estimation is required here because thedata frame portion 36 of packet 30 does not contain data symbols whichare known a-priori at the receiver.

At step 930, slice the estimated data symbol, â_(k,I) to the nearestconstellation point:

At step 940, estimate the noise variance at frequency bin k for the ithOFDM frame as{circumflex over (σ)}_(k,i) ² =|r _(k,i) −Ĥ _(k) â _(k,i)|²

At step 950, average the variance estimates as follows:

${{\hat{\sigma}}_{k}^{2} = {{\frac{1}{N_{f} + 1}{\sum\limits_{i = 0}^{Nj}\;{{\hat{\sigma}}_{k,i}^{2}k}}} = {1_{n}\mspace{11mu}\ldots}}}\mspace{11mu},N$

Where: N_(f) is the number of OFDM frames used for averaging theestimate.

At step 960, the channel and noise estimates obtained at steps 900 and950, respectively, may now be used in equations (12) and (14) todetermine soft-metrics for use in the Viterbi decoder 71 of FIG. 5.

SECOND EMBODIMENT FOR PROVIDING AN IMPROVED NOISE ESTIMATE

In accordance with a second embodiment for providing an improvedestimate of noise variance, to further enhance the noise varianceestimate than what can be achieved in the prior embodiment, it ispossible to employ an advanced slicer and noise variance estimation unitas a substitute for the basic slicer and noise variance estimation unit68 of the receiver 60 of FIG. 8. The advanced slicer works on theprinciple of deriving better estimates for the data symbols byre-encoding and decoding the received data symbols over some number ofiterations such that each subsequent iteration provides a betterestimate of the received data symbol which may then be used to derive abetter estimate of the noise variance.

FIG. 9 a is a diagram of the receiver 60 of FIG. 5 modified toincorporate an advanced slicer in accordance with the presentembodiment. In the modified receiver 70 of FIG. 9, the advanced slicerand noise variance estimation unit 81 substitutes for the basic slicerand noise variance estimation unit 68 of FIG. 5.

FIG. 9 b is a block diagram illustrating in more detail the constructionof the advanced slicer and noise variance estimation unit 81 of receiver70. As shown the advanced slicer and noise variance estimation unit 81is made up of two components, the advanced slicer 84 and the noisevariance estimator 85. The advanced slicer 84 is further comprised oftwo components, a decoding block 82 whose output is coupled to the inputof a re-encoding block 83. In this manner, the data symbols of theserial data bit stream received at point ‘A’ are decoded and thenre-encoded to output a serial data bit stream, at point ‘B’, includingmore accurate reference data symbols for the noise variance estimator84.

FIG. 10 is a flowchart describing the steps for obtaining a more refinednoise estimate in accordance with the present embodiment.

The flowchart of FIG. 10 repeats steps 900-950 of the flowchart of FIG.8 and such will not be further described. In addition to the knownsteps, the flowchart of FIG. 10 modifies step 960 and includesadditional steps 970 and 980 which define the operations of the advancedslicer and noise variance estimator (block 81) as illustrated in FIGS. 9a and 9 b.

With reference to the flowchart of FIG. 10, starting with step 960, amore refined noise estimate is obtained by using the averaged noisevariance estimate obtained at step 950 and the channel estimate fromstep 900 to determine soft-metrics for the data portion (data symbols)36 of received OFDM packet 30. The soft-metrics are computed in theadvanced slicer and noise variance estimator unit 81 of FIG. 9 a. Moreparticularly, the soft-metrics are computed in the bit metric-unit 82 aof advanced slicer unit 81. Then, the computed soft-metric values arede-interleaved at block 82 band supplied to the Viterbi decoder 82 c, atstep 970. The decoding operations described 82 a, 82 b, 82 ccollectively comprise the decoding block 82 of the advanced slicer 81.Thereafter, at step 980, the output of the decoding block 82 is suppliedas input to the re-encoding block 83 to re-encode the once-decoded databits. As shown in the flowchart, the re-encoded data bits are thensupplied as input to block 940 to estimate the noise variance againusing the decoded/re-encoded data bits in the feedback loop 960-980. Itis noted that this feedback loop may be used for any number ofiterations necessary to obtain a noise variance estimate which meets orexceeds a certain prescribed threshold.

As is apparent from the foregoing, the present invention has anadvantage in that it is possible for a receiver in an 802.11g WLANsystem to estimate the unknown multi-path channel and the interferencevariance simultaneously without a prior knowledge of the location of theinterferer in the band and use the information to generate soft-metricsfor a Viterbi decoder.

1. In a wireless local area network (WLAN), a method for estimating anunknown multi-path channel and a noise variance in the presence ofnarrowband interference, said method comprising the steps of: (a)receiving a time domain OFDM data packet; (b) converting said timedomain OFDM data packet to a frequency domain OFDM data packet; (c)extracting a vector of training symbols having known transmitted valuesfrom said frequency domain OFDM data packet; (d) using said trainingsymbols to derive a simplified channel estimate that assumes nointerference is present in the unknown multi-path channel, and (e)estimating a noise variance of said narrowband interference using saidsimplified channel estimate at said step (d).
 2. The method of claim 1,wherein said WLAN is operated in accordance with the IEEE 802.11standard.
 3. The method of claim 1, wherein said step (d) of derivingsaid simplified channel estimate further comprises the steps of: (1)recognizing a time-frequency relationship of a channel impulse responsein the time domain to a channel impulse response in the frequency domainas:H=Fh (2) using the recognized time-frequency relationship, H=Fh, toderive a matrix solution of a received signal model in the frequencydomain as:r=A(Fh )+ n where F is an N×Nc truncated Fourier matrix; h is thechannel impulse response in the time domain; A is an N×N diagonal matrixcomprised of said plurality of known transmitted data symbols; and n isthe noise vector; (3) calculating a least squares estimate of thechannel impulse response H as:H _(LS) =F(G ^(H) R _(n) ⁻¹ G)⁻¹ G ^(H) R _(n) ⁻¹ r (4) neglecting anoise correlation matrix term R_(n) ⁻¹ of the calculated least squaresestimate of the channel impulse response H at step (3) to compute saidsimplified channel estimate in the frequency domain as:H _(LS) =F(G ^(H) G)⁻¹ G ^(H) r where F and A and G=AF are matrix valueswhich are all known a-priori for long training sequences L1 and L2 at areceiving node in said WLAN.
 4. The method of claim 2, where said step(e) of estimating said noise variance further comprises the steps of:computing an error vector e as:e=r−AĤ _(LS) and calculating said noise variance estimate as:σ_(k) ² =|e _(k)|².
 5. In a wireless local area network (WLAN), a methodfor estimating an unknown multi-path channel and a noise variance in thepresence of narrowband interference, said method comprising the stepsof: (a) receiving a time domain OFMD data packet; (b) converting saidtime domain OFDM data packet from said time domain to a frequency domainOFDM data packet; (c) using training symbols from long trainingsequences L1 and L2 contained within said OFDM data packet to derive asimplified channel estimate in frequency as:H _(LS) =F(G ^(H) G)⁻¹ G ^(H) r where F and A and G=AF are matrix valueswhich are all known a-priori for said long training sequences L1 and L2at a receiving node in said WLAN; (d) estimating a noise variance ofsaid narrowband interference using said simplified channel estimate atsaid step (a), comprising the steps of: (1) computing an error vector eas:e=r−AĤ _(LS); and (2) calculating said noise variance estimate as:σ_(k) ² =|e _(k)|²; (e) estimating a transmitted symbol asa _(k,I) =r _(k,I) /Ĥ _(k) (f) slicing said estimated transmitted symbola_(k,I) to the nearest constellation point; (g) estimating the noisevariance at frequency k as:{circumflex over (σ)}_(k,i) ² =|r _(k,i) −Ĥ _(k) â _(k,i)|² (h)averaging the noise variance estimate over N OFDM data frames to obtaina more refined noise variance estimate as:${{\hat{\sigma}}_{k}^{2} = {{\frac{1}{N_{f} + 1}{\sum\limits_{i = 0}^{Nj}\;{{\hat{\sigma}}_{k,i}^{2}k}}} = {1_{n}\mspace{11mu}\ldots}}}\mspace{11mu},{N.}$6. The method of claim 5, wherein said a more refined averaged noisevariance estimate than that obtained at said step (d) is computed as:σ_(k) ² =W _(L)σ_(k,0) ² +W ₀ /NfΣσ _(k,i) ² k=1,2, . . . , 48 whereW_(L)+W₀=1 W_(L)=a weight corresponding to a long training sequence,e.g., L₁, L₂; W₀=a weight corresponding to one or more data frames. 7.The method of claim 5, further comprising the steps of: (i) decoding thesliced estimated transmitted symbol a_(k,I); (j) re-encoding the decodedsymbol at said step (e); and (k) repeating said steps (g) through (j)for N iterations to derive a more refined noise variance estimate thanthe one obtained at said step (d).
 8. In a wireless local area network(WLAN), a system for estimating an unknown multi-path channel and anoise variance in the presence of narrowband interference, said systemcomprising: means for receiving a time domain OFDM data packet; meansfor converting said time domain OFDM data packet to a frequency domainOFDM data packet; means for extracting a vector of training symbolshaving known transmitted values from said frequency domain OFDM datapacket; means for using said training symbols to derive a simplifiedchannel estimate that assumes no interference in said unknown multi-pathchannel; and means for estimating a noise variance of said narrowbandinterference using said simplified channel estimate at said step (d). 9.The system of claim 8, wherein said WLAN is operated in accordance withthe IEEE 802.11 standard.
 10. The system of claim 8, wherein said meansfor using said training symbols to derive a simplified channel estimate,further comprises: means for recognizing a time-frequency relationshipof a channel impulse response in the time domain to a channel impulseresponse in the frequency domain as:H=Fh means for using the recognized time-frequency relationship, H=Fh,to derive a matrix solution of a received signal model in the frequencydomain as:r=A(Fh )+ n where F is an N×Nc truncated Fourier matrix; h is thechannel impulse response in the time domain; A is an N×N diagonal matrixcomprised of said plurality of known transmitted data symbols; and n isthe noise vector; means for calculating a least squares estimate of thechannel impulse response H as:H _(LS) =F(G ^(H) R _(n) ⁻¹ G)⁻¹ G ^(H) R _(n) ⁻¹ r means for neglectinga noise correlation matrix term R_(n) ⁻¹ of the calculated least squaresestimate of the channel impulse response H at step (3) to compute saidsimplified channel estimate in the frequency domain as:H _(LS) =F(G ^(H) G)⁻¹ G ^(H) r where F and A and G=AF are matrix valueswhich are all known a-priori for long training sequences L1 and L2 at areceiving node in said WLAN.
 11. The method of claim 10, where saidestimation of said noise variance further comprises: computing an errorvector e as:e=r−AĤ _(LS); and calculating said noise variance estimate as:σ_(k) ² =|e _(k)|².
 12. In a wireless local area network (WLAN), asystem for estimating an unknown multi-path channel and a noise variancein the presence of narrowband interference, said system comprising: meanfor receiving a time domain OFMD data packet; means for converting saidtime domain OFDM data packet from said time domain to a frequency domainOFDM data packet; means for using training symbols from long trainingsequences L1 and L2 contained within said OFDM data packet to derive asimplified channel estimate in frequency as:H _(LS) =F(G ^(H) G)⁻¹ G ^(H) r, where F and A and G=AF are matrixvalues which are all known a-priori for said long training sequences L1and L2 at a receiving node in said WLAN; means for estimating a noisevariance of said narrowband interference using said simplified channelestimate at said step (a), comprising the steps of: (1) computing anerror vector e as:e=r−AH _(LS); and (2) calculating said noise variance estimate as:σ_(k) ² =|e _(k)|²; means for estimating a transmitted symbol asa _(k,I) =r _(k,I) /Ĥ _(k) means for slicing said estimated transmittedsymbol a_(k,I) to the nearest constellation point; means for estimatingthe noise variance at frequency k as:{circumflex over (σ)}_(k,i) ² =|r _(k,i) −Ĥ _(k) â _(k,i)|² means foraveraging the noise variance estimate over N OFDM data frames to obtaina more refined noise variance estimate as:${{\hat{\sigma}}_{k}^{2} = {{\frac{1}{N_{f} + 1}{\sum\limits_{i = 0}^{Nj}\;{{\hat{\sigma}}_{k,i}^{2}k}}} = {1_{n}\mspace{11mu}\ldots}}}\mspace{11mu},{N.}$13. The system of claim 12, wherein said a more refined averaged noisevariance estimate is computed as:σ_(k) ² =W _(L)σ_(k,0) ² +W ₀ /NfΣσ _(k,i) ² K=1,2, . . . , 48 whereW_(L)+W₀=1 W_(L)=a weight corresponding to a long training sequence,e.g., L₁, L₂; W₀=a weight corresponding to one or more data frames. 14.The system of claim 12, further comprising: means for decoding thesliced estimated transmitted symbol a_(k,I); means or re-encoding thedecoded symbol at said step (e); and means for repeating said steps (g)through (j) for N iterations to derive a more refined noise varianceestimate than the one obtained at said step (d).
 15. The method of claim1, wherein using the training symbols to derive a simplified channelestimate comprises calculating a channel impulse response frequencymatrix for each frequency bin in the frequency domain OFDM data packetassuming that all noise in the channel is white Gaussian noise with zeromean and variance.
 16. The system of claim 8, wherein the means forusing the training symbols to derive a simplified channel estimatecalculates a channel impulse response frequency matrix for eachfrequency bin in the frequency domain OFDM data packet assuming that allnoise in the channel is white Gaussian noise with zero mean andvariance.